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diff --git a/elki/src/main/java/de/lmu/ifi/dbs/elki/utilities/scaling/outlier/OutlierLinearScaling.java b/elki/src/main/java/de/lmu/ifi/dbs/elki/utilities/scaling/outlier/OutlierLinearScaling.java
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--- /dev/null
+++ b/elki/src/main/java/de/lmu/ifi/dbs/elki/utilities/scaling/outlier/OutlierLinearScaling.java
@@ -0,0 +1,332 @@
+package de.lmu.ifi.dbs.elki.utilities.scaling.outlier;
+
+/*
+ This file is part of ELKI:
+ Environment for Developing KDD-Applications Supported by Index-Structures
+
+ Copyright (C) 2015
+ Ludwig-Maximilians-Universität München
+ Lehr- und Forschungseinheit für Datenbanksysteme
+ ELKI Development Team
+
+ This program is free software: you can redistribute it and/or modify
+ it under the terms of the GNU Affero General Public License as published by
+ the Free Software Foundation, either version 3 of the License, or
+ (at your option) any later version.
+
+ This program is distributed in the hope that it will be useful,
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+ MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ GNU Affero General Public License for more details.
+
+ You should have received a copy of the GNU Affero General Public License
+ along with this program.  If not, see <http://www.gnu.org/licenses/>.
+ */
+
+import java.util.ArrayList;
+
+import de.lmu.ifi.dbs.elki.database.ids.DBIDIter;
+import de.lmu.ifi.dbs.elki.database.relation.DoubleRelation;
+import de.lmu.ifi.dbs.elki.math.DoubleMinMax;
+import de.lmu.ifi.dbs.elki.math.MeanVariance;
+import de.lmu.ifi.dbs.elki.result.outlier.OutlierResult;
+import de.lmu.ifi.dbs.elki.utilities.datastructures.arraylike.NumberArrayAdapter;
+import de.lmu.ifi.dbs.elki.utilities.optionhandling.AbstractParameterizer;
+import de.lmu.ifi.dbs.elki.utilities.optionhandling.OptionID;
+import de.lmu.ifi.dbs.elki.utilities.optionhandling.constraints.GlobalParameterConstraint;
+import de.lmu.ifi.dbs.elki.utilities.optionhandling.constraints.OnlyOneIsAllowedToBeSetGlobalConstraint;
+import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameterization.Parameterization;
+import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameters.DoubleParameter;
+import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameters.Flag;
+import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameters.Parameter;
+
+/**
+ * Scaling that can map arbitrary values to a value in the range of [0:1].
+ * 
+ * Transformation is done by linear mapping onto 0:1 using the minimum and
+ * maximum values.
+ * 
+ * @author Erich Schubert
+ */
+public class OutlierLinearScaling implements OutlierScalingFunction {
+  /**
+   * Parameter to specify a fixed minimum to use.
+   * <p>
+   * Key: {@code -linearscale.min}
+   * </p>
+   */
+  public static final OptionID MIN_ID = new OptionID("linearscale.min", "Fixed minimum to use in linear scaling.");
+
+  /**
+   * Parameter to specify the maximum value.
+   * <p>
+   * Key: {@code -linearscale.max}
+   * </p>
+   */
+  public static final OptionID MAX_ID = new OptionID("linearscale.max", "Fixed maximum to use in linear scaling.");
+
+  /**
+   * Flag to use the mean as minimum for scaling.
+   * 
+   * <p>
+   * Key: {@code -linearscale.usemean}
+   * </p>
+   */
+  public static final OptionID MEAN_ID = new OptionID("linearscale.usemean", "Use the mean as minimum for scaling.");
+
+  /**
+   * Flag to use ignore zeros when computing the min and max.
+   * 
+   * <p>
+   * Key: {@code -linearscale.ignorezero}
+   * </p>
+   */
+  public static final OptionID NOZEROS_ID = new OptionID("linearscale.ignorezero", "Ignore zero entries when computing the minimum and maximum.");
+
+  /**
+   * Field storing the Minimum to use
+   */
+  protected Double min = null;
+
+  /**
+   * Field storing the Maximum value
+   */
+  protected Double max = null;
+
+  /**
+   * Scaling factor to use (1/ max - min)
+   */
+  double factor;
+
+  /**
+   * Use the mean for scaling
+   */
+  boolean usemean = false;
+
+  /**
+   * Ignore zero values
+   */
+  boolean nozeros = false;
+
+  /**
+   * Constructor.
+   */
+  public OutlierLinearScaling() {
+    this(null, null, false, false);
+  }
+
+  /**
+   * Constructor.
+   * 
+   * @param min
+   * @param max
+   * @param usemean
+   * @param nozeros
+   */
+  public OutlierLinearScaling(Double min, Double max, boolean usemean, boolean nozeros) {
+    super();
+    this.min = min;
+    this.max = max;
+    this.usemean = usemean;
+    this.nozeros = nozeros;
+    if (min != null && max != null) {
+      this.factor = (max - min);
+    }
+  }
+
+  @Override
+  public double getScaled(double value) {
+    assert (factor != 0) : "prepare() was not run prior to using the scaling function.";
+    if (value <= min) {
+      return 0;
+    }
+    return Math.min(1, ((value - min) / factor));
+  }
+
+  @Override
+  public void prepare(OutlierResult or) {
+    if (usemean) {
+      MeanVariance mv = new MeanVariance();
+      DoubleMinMax mm = (max == null) ? new DoubleMinMax() : null;
+      boolean skippedzeros = false;
+      DoubleRelation scores = or.getScores();
+      for (DBIDIter id = scores.iterDBIDs(); id.valid(); id.advance()) {
+        double val = scores.doubleValue(id);
+        if (nozeros && val == 0.0) {
+          skippedzeros = true;
+          continue;
+        }
+        if (!Double.isNaN(val) && !Double.isInfinite(val)) {
+          mv.put(val);
+        }
+        if (max == null) {
+          mm.put(val);
+        }
+      }
+      if (skippedzeros && mm.getMin() == mm.getMax()) {
+        mm.put(0.0);
+        mv.put(0.0);
+      }
+      min = mv.getMean();
+      if (max == null) {
+        max = mm.getMax();
+      }
+    } else {
+      if (min == null || max == null) {
+        boolean skippedzeros = false;
+        DoubleMinMax mm = new DoubleMinMax();
+        DoubleRelation scores = or.getScores();
+        for (DBIDIter id = scores.iterDBIDs(); id.valid(); id.advance()) {
+          double val = scores.doubleValue(id);
+          if (nozeros && val == 0.0) {
+            skippedzeros = true;
+            continue;
+          }
+          mm.put(val);
+        }
+        if (skippedzeros && mm.getMin() == mm.getMax()) {
+          mm.put(0.0);
+        }
+        if (min == null) {
+          min = mm.getMin();
+        }
+        if (max == null) {
+          max = mm.getMax();
+        }
+      }
+    }
+    factor = (max - min);
+  }
+
+  @Override
+  public <A> void prepare(A array, NumberArrayAdapter<?, A> adapter) {
+    if (usemean) {
+      MeanVariance mv = new MeanVariance();
+      DoubleMinMax mm = (max == null) ? new DoubleMinMax() : null;
+      boolean skippedzeros = false;
+      final int size = adapter.size(array);
+      for (int i = 0; i < size; i++) {
+        double val = adapter.getDouble(array, i);
+        if (nozeros && val == 0.0) {
+          skippedzeros = true;
+          continue;
+        }
+        if (!Double.isNaN(val) && !Double.isInfinite(val)) {
+          mv.put(val);
+        }
+        if (max == null) {
+          mm.put(val);
+        }
+      }
+      if (skippedzeros && mm.getMin() == mm.getMax()) {
+        mm.put(0.0);
+        mv.put(0.0);
+      }
+      min = mv.getMean();
+      if (max == null) {
+        max = mm.getMax();
+      }
+    } else {
+      if (min == null || max == null) {
+        boolean skippedzeros = false;
+        DoubleMinMax mm = new DoubleMinMax();
+        final int size = adapter.size(array);
+        for (int i = 0; i < size; i++) {
+          double val = adapter.getDouble(array, i);
+          if (nozeros && val == 0.0) {
+            skippedzeros = true;
+            continue;
+          }
+          mm.put(val);
+        }
+        if (skippedzeros && mm.getMin() == mm.getMax()) {
+          mm.put(0.0);
+        }
+        if (min == null) {
+          min = mm.getMin();
+        }
+        if (max == null) {
+          max = mm.getMax();
+        }
+      }
+    }
+    factor = (max - min);
+  }
+
+  @Override
+  public double getMin() {
+    return 0.0;
+  }
+
+  @Override
+  public double getMax() {
+    return 1.0;
+  }
+
+  /**
+   * Parameterization class.
+   * 
+   * @author Erich Schubert
+   * 
+   * @apiviz.exclude
+   */
+  public static class Parameterizer extends AbstractParameterizer {
+    /**
+     * Field storing the Minimum to use
+     */
+    protected Double min = null;
+
+    /**
+     * Field storing the Maximum value
+     */
+    protected Double max = null;
+
+    /**
+     * Use the mean for scaling
+     */
+    boolean usemean = false;
+
+    /**
+     * Ignore zero values
+     */
+    boolean nozeros = false;
+
+    @Override
+    protected void makeOptions(Parameterization config) {
+      super.makeOptions(config);
+      DoubleParameter minP = new DoubleParameter(MIN_ID);
+      minP.setOptional(true);
+      if (config.grab(minP)) {
+        min = minP.getValue();
+      }
+
+      DoubleParameter maxP = new DoubleParameter(MAX_ID);
+      maxP.setOptional(true);
+      if (config.grab(maxP)) {
+        max = maxP.getValue();
+      }
+
+      Flag meanF = new Flag(MEAN_ID);
+      if (config.grab(meanF)) {
+        usemean = meanF.getValue();
+      }
+
+      Flag nozerosF = new Flag(NOZEROS_ID);
+      if (config.grab(nozerosF)) {
+        nozeros = nozerosF.getValue();
+      }
+
+      // Use-Mean and Minimum value must not be set at the same time!
+      ArrayList<Parameter<?>> minmean = new ArrayList<>();
+      minmean.add(minP);
+      minmean.add(meanF);
+      GlobalParameterConstraint gpc = new OnlyOneIsAllowedToBeSetGlobalConstraint(minmean);
+      config.checkConstraint(gpc);
+    }
+
+    @Override
+    protected OutlierLinearScaling makeInstance() {
+      return new OutlierLinearScaling(min, max, usemean, nozeros);
+    }
+  }
+}